Modelling an influenza pandemic: A guide for the perplexed by ProQuest

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                          Modelling an influenza pandemic: A guide for the perplexed

                          Pandemic Influenza Outbreak Research Modelling Team (Pan-InfORM)

                          @@       See other H1N1 articles: Editorial, page 123; Research, page 159




                          A
                                    new swine-origin influenza A (H1N1) virus, ini-
                                                                                                      Key points
                                    tially identified in Mexico, has now caused out-
                                    breaks of disease in at least 74 countries, with decla-           •   When a new infectious disease emerges, mathematical
                                                                                                          models can project plausible scenarios, guide control
                          ration of a global influenza pandemic by the World Health                       strategies and identify important areas for urgent research.
                          Organization on June 11, 2009.1 Optimizing public health                    •   Models of influenza pandemics suggest roles for antiviral
                          responses to this new pathogen requires difficult decisions                     drugs and vaccines. Models also raise concerns about
                          over short timelines. Complicating matters is the unpre-                        antiviral resistance.
                          dictability of influenza pandemics: planners cannot base                    •   Knowledge translation is a key part of modelling activities
                          their decisions solely on pre-pandemic factors or on experi-                    that aim to optimize policy decisions for containment of
                          ence from earlier pandemics. We suggest that mathematical                       new infectious diseases.
                          modelling can inform and optimize health policy decisions
                          in this situation.
                                                                                                       As an example of synthesizing data, consider the process
                          Uses of models                                                            of developing a mathematical model of the effectiveness of
                                                                                                    influenza vaccines: modellers must draw together information
                          Mathematical models of infectious diseases are useful tools               on influenza epidemiology (including patterns of spread in
                          for synthesizing the best available data on a new pathogen,               different age groups), the natural history of influenza, the
                          comparing control strategies and identifying important areas              effectiveness of vaccines in randomized trials and the dura-
                          of uncertainty that may be prioritized for urgent research.               tion of immunity following vaccination or natural infection,2,3
                                                                                                    which cannot all be derived from a single study. Once the
                                   Generation                                                       model is developed, rapid and inexpensive “experiments” can
                                                                                                    be performed by simulating alternative vaccination strategies
                               0        1        2                                                  (e.g., vaccinating children most likely to transmit influenza,
                                                                                                    or vaccinating older adults most likely to have severe compli-
                                                                                                    cations of influenza).2
                                                                                                       The uncertainty involved in this process can be assessed
                                                                                                    through sensitivity analysis (in this case, by varying estimates
                                                                                                    of vaccine effectiveness across plausible ranges) to examine
                                                                                                    whether such variation results in markedly different out-
                                                                                                    comes. Uncertain model inputs that are extremely influential
                                                                                                    in determining the best course of action should be prioritized
                                                                                                    for future research.

                            Initial phase of epidemic (R0 = 3)      Disease is endemic (R = 1)      Elements of models
                                                                                                    Elements of epidemic models often include “compart-
                           Figure 1: The number of new infections generated when the                ments” or “states” that describe the susceptibility, infec-
                           basic reproductive number (the number of new cases created
                           by a single primary case in a susceptible population) is 3. Cases
                                                                                                    tiousness or immunity of individuals in a population, and
                           of disease are represented as dark circles, and immune individ-          “parameters” (numbers) that describe how individuals
                   
								
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